The type of missing data in this scenario would be classified as "Missing Not at Random" (MNAR). This is because the missingness of the data (the battery running out) is related to the values of the data itself (the measurements of the new type of acid).

Here are the steps to identify this:

1. Understand the types of missing data: There are three types of missing data - Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR).

2. Analyze the scenario: In this case, the batteries running out for a batch of samples during an exam on a new type of acid.

3. Identify the type of missing data: The missing data is not completely random because it is not due to a random occurrence, but a specific event (batteries running out). It's also not missing at random because the missingness is related to the potential values of the data. Therefore, it is Missing Not at Random (MNAR).

Question

The type of missing data in this scenario would be classified as "Missing Not at Random" (MNAR). This is because the missingness of the data (the battery running out) is related to the values of the data itself (the measurements of the new type of acid).

Here are the steps to identify this:

1. Understand the types of missing data: There are three types of missing data - Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR).

2. Analyze the scenario: In this case, the batteries running out for a batch of samples during an exam on a new type of acid.

3. Identify the type of missing data: The missing data is not completely random because it is not due to a random occurrence, but a specific event (batteries running out). It's also not missing at random because the missingness is related to the potential values of the data. Therefore, it is Missing Not at Random (MNAR).

Knowee AI · Accepted Answer

The type of missing data in this scenario would be classified as "Missing Not at Random" (MNAR). This is because the missingness of the data (the battery running out) is related to the values of the data itself (the measurements of the new type of acid).

Here are the steps to identify this:

1. Understand the types of missing data: There are three types of missing data - Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR).

2. Analyze the scenario: In this case, the batteries running out for a batch of samples during an exam on a new type of acid.

3. Identify the type of missing data: The missing data is not completely random because it is not due to a random occurrence, but a specific event (batteries running out). It's also not missing at random because the missingness is related to the potential values of the data. Therefore, it is Missing Not at Random (MNAR).

When conducting an exam on a new type of acid, the batteries ran out for a batch of samples, what kind of missing data will be present in this exam?

Question

When conducting an exam on a new type of acid, the batteries ran out for a batch of samples, what kind of missing data will be present in this exam?

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